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Purpose The purpose of this study is to investigate whether within the same firm, earnings risk is exacerbated in the fiscal year end (FYE) quarters relative to that of other quarters, more importantly, if this type of earnings risk is unique. Further, the authors discuss solutions to mitigate this type of information risk. Design/methodology/approach This study provides evidence that the information risk associated with FYE quarter earnings cannot be explained by other identified risk factors. Solutions to mitigate this risk include strong corporate governance and a more streamlined financial reporting structure. Findings The paper shows that there is significantly lower earnings response coefficient for FYE quarters than for non-FYE quarters (1984–2015). Furthermore, strong corporate governance and a more streamlined financial reporting structure, either by firms willingly reducing the usage of extraordinary item reporting or by FASB codification changes such as FASB 145, can help mitigate this type of information uncertainty. Research limitations/implications This study explains that the causes of the exacerbated information risk associated with FYE quarter earnings identified in prior literature, namely, the “integral explanation” and “manipulation explanation,” are not mutually exclusive. Therefore, the authors deem it futile to disentangle the two. Instead, the authors offer two possible solutions.

A well-known asset pricing anomaly, the “MAX” effect, measured by the maximum daily return in the past month, depicts stocks’ lottery-like features and investor gambling behaviour. Using the comprehensive stock-level Dow Jones (DJNS) news database between 1979 and 2016, we consider in a empirical setting how the presence of news reports affects these lottery-type stocks. We find an augmented negative relationship between MAX stocks without news and expected returns, whereby MAX with news coverage generates return momentum. The differing future return relationships between MAX stocks with and without news appears to be best explained by information uncertainty mitigation upon news arrival. Overall, our findings suggest that news plays a role in resolving information uncertainty in the stock market.

This study shows that, to obtain a precise measure of the liquidity premium in the stock market, it is important to recognize the influence of information uncertainty on the pricing of liquidity. Information uncertainty, which is positively correlated with stock illiquidity but negatively priced in the stock market, obscures the estimation of the liquidity premium. After controlling for its influence, we find that the liquidity premium is statistically significant and economically important in the U.S. stock market. Moreover, the risk-adjusted liquidity premium remains significant in both the earlier and more recent sub-sample periods. Our study addresses the recent debate about whether liquidity is still priced in recent decades, given the significant improvement in the trading technology and increase of the trading volume during this period.

PurposeThis study aims to examine individuals' tendency to strictly follow their own signal while ignoring predecessors' decisions when making decisions under varying degrees of uncertainty.Design/methodology/approachUsing a controlled laboratory experiment, the authors separate the follow-own-signal behavior from other types of behavior such as Bayes consistent or herd-like (i.e. follow-the-majority) behavior.FindingsAs the authors systemically increase the degree of uncertainty in the information environment, participants are increasingly more likely to act only on their own signal. This suggests that financial decisions that are made under highly uncertain market conditions may be more signal revealing, and hence, may lead to better information aggregation than previously thought. The authors also find that as uncertainty increases, participants are more likely to switch in and out of this behavior, suggesting that behavior under highly uncertain conditions may also be more random and complex.Originality/valueThe authors are the first to examine how uncertainty affects the follow-own-signal behavior. The authors also offer potential testable empirical implications, such as an increase in contrarian investing, home bias, and own-company ownership under times of increased uncertainty or in more uncertain markets.

We use clickstream data to show that investors’ demand for information about macroeconomic factors affecting the path of future interest rates is a measure of their uncertainty about this path. In particular, an increase in information demand ahead of influential economic announcements affecting investors’ beliefs about future interest rates predicts a stronger reaction of U.S. Treasury note yields to these announcements, as it should if information demand positively covaries with uncertainty. This relationship does not vanish after using standard measures of uncertainty as predictors, suggesting that clickstream data contain unique information about investors’ uncertainty.

The green vehicle routing problem of distributing gasoline to retail gasoline stations in a way that is not just the most cost-effective overall but also reduces its own carbon emissions needs to a...

This paper examines how ambiguity aversion and information uncertainty jointly influence the momentum effect across countries. Most countries in our sample exhibit information uncertainty effect, i.e., the momentum effect is stronger for firms with greater uncertainty (e.g., smaller, younger, and more volatile firms). Moreover, we find that such information uncertainty effect is stronger in countries with lower degree of ambiguity aversion. The interaction effect of uncertainty and ambiguity aversion is mainly driven by losers, suggesting the information avoidance or “Ostrich Effect”. We offer two possible explanations for this result based on the potential impacts of ambiguity aversion on information reaction and information seeking.

In this paper we contribute to a line of literature that examines behavioral biases that impact important corporate decisions. Our paper builds on prior articles that examine heaping or rounding of EPS forecasts. Herrmann and Thomas (2005), Bamber, Hui and Yeung (2010) and Dechow and You (2012) observe that firm managers and analysts systematically round EPS forecasts. They also find more rounding for EPS forecasts with larger magnitude, and when management or analysts face higher levels of information uncertainty. In this study we examine whether dividend policy is also affected by rounding and the heaping heuristic. The previous literature that examines heaping related to corporate earnings focuses on heaping in earnings forecasts, however our study focuses on heaping in actual distributed dividends that directly impact key firm characteristics such as firm capital structure, investor returns and overall firm performance. From 1926 through 2015 we report 51.40% of dividends are heaped. In the earlier years, dividends are almost always heaped, but heaping declines through time. In some years 100% of quarterly dividends are heaped. We find heaping is significantly related to dividend size, and the level of information uncertainty faced by firm management. Of the uncertainty metrics, inverted firm size has the strongest relation with the likelihood of heaping. We also observe that heaped dividends are altered less frequently, and when there is a dividend change to a previously heaped dividend it is more often an increase or decrease to another heaped amount.

Anecdotal evidence seems to suggest that the initial public offering (IPO) market performed remarkably well through the COVID-19 pandemic. To further understand this peculiar observation, we carry out a comprehensive analysis of IPOs during the pandemic vis-a-vis IPOs before the pandemic. Our findings imply that IPOs during the pandemic experience greater information uncertainty compared to those before the pandemic, and this greater uncertainty is mainly driven by the IPOs from the high-technology and the healthcare sectors. Furthermore, we find that an average IPO firm experiences larger underpricing and more post-IPO return volatility as the pandemic and the associated government responses increase in severity before the offering. Overall, our study indicates that the COVID-19 pandemic had an adverse impact on the IPO market.

This paper empirically investigates how firm-level information uncertainty impacts momentum profits in the Chinese Class A share market. We employ seven different factors to gauge the degree of firm-level information uncertainty—firm size, firm age, analysts’ coverage, return volatility, dispersion in analysts’ earnings forecast, trading volume, and the quality/strength of corporate governance (free float ratio). We find evidence showing that information uncertainty has an amplifying effect over the momentum profits, and the amplifying effect is more pronounced over the time periods following DOWN market state over the sample period from January 1996 to December 2013. The robustness of the empirical evidence is warranted by a risk-adjustment test based on the FF3F model and Wang and Xu’s (Financ Anal J 60(6):65–77, 2004) FF3F model, a sub-period analysis, and a different definition of market states. The empirical findings can provide an important reference point for international and domestic investors when adjusting investment strategies and portfolio positions in relatively volatile financial markets such as the Chinese stock market.

We examine how a target’s information uncertainty level affects an acquirer’s valuation of the target and the acquirer’s gain realized from the transaction. Based on a simple perpetual discounted cash flow model, we argue that the valuation will be lower for a target with higher information uncertainty and acquiring a target with high information uncertainty can potentially create value for an acquirer’s shareholders. The empirical findings lend support to our arguments. Specifically, we observe that a target’s valuation multiple obtained from an acquirer is negatively correlated with the target’s information uncertainty level. An acquirer’s announcement return is negatively correlated with the valuation multiple given to the target but positively correlated with the target’s information uncertainty level. The results are robust against various measurements of valuation multiples and information uncertainty.

This study investigates the relationships among information uncertainty, investor sentiment, analyst reports, and stock returns in a unified framework. The effects of analyst reports on stock returns depend on the degrees of information uncertainty, indicating that recommendation upgrades (downgrades) convey more valuable positive (negative) information under higher information uncertainty. Such stock market reactions are significantly explained by investor sentiment when information uncertainty is high. Our empirical findings are robust to changes in abnormal return measures and information uncertainty proxies.

An implicit premise of much empirical research is that the individual role and the collective role of a financial reporting quality (quality) measure are the same, in the sense that the impacts of ...

This study investigates whether and when differences in the credit rating agencies' methodologies result in differences in rating properties. In particular, this study focuses on differences in information processing constraints between a rating agency that utilizes qualitative analysis and direct access to borrowers' management in its rating process (Standard & Poor's) compared to one that does not (Egan Jones Ratings Company) and how these differences affect rating quality. We find that as information uncertainty about borrowers increases, Egan Jones's rating accuracy, informativeness, and timeliness decrease relative to Standard & Poor's. Our findings suggest that Egan Jones's more restricted rating methodology can lead to limitations in information processing and, thus, reductions in Egan Jones's rating quality advantage for borrowers with greater information uncertainty. JEL Classifications: G10; G24.

Using a sample of Taiwan stock market, this paper investigates the role of information uncertainty in the profitability of technical analysis by applying a moving average (MA) strategy to portfolios grouped according to whether firms issue stock options. Results indicate that, even though considering transaction costs, the MA strategy significantly outperforms the buy-and-hold strategy on the portfolio without option issuance, but not on the portfolio with option issuance. The results support the hypothesis that stocks that do not issue options exhibit greater information uncertainty, and thus greater price continuation, which in turn implies a superior performance of the MA strategy.

We investigate whether CEOs strategically increase information uncertainty surrounding their insider stock purchases. We find that in the month before and during CEO stock purchases, information uncertainty in their news releases increases, which correlates with lower stock prices. This finding is not evident in the month after insider purchases and thus suggests CEOs’ strategic behavior. Our key findings of strategic information uncertainty are confirmed using multi-faceted fixed effects regressions, a quasi-natural experimental design, and numerous other robustness checks.

We document that acquirer announcement returns decrease and takeover premiums increase with the length of time since targets’ initial public offerings. Declining asymmetric information that leads to lower target valuation uncertainty for the acquirer can explain these effects. Newly public firms should have greater information asymmetry than established public firms that makes the target valuation more uncertain for a less-informed acquirer. Risk-averse acquirer managers pay less for riskier targets, resulting in relatively low takeover premiums and high acquirer announcement returns. Over time, as a target builds a public track record, the asymmetric information about its valuation declines, and takeover premiums increase to the benefit of target shareholders and the detriment of acquirer shareholders.

Many airports are being expanded from transportation centers to economic hubs. This new type of urban area has been termed the aerotropolis or airport metropolis and is meant to function as an economic center with land-use that link local and global markets. However, to find the optimal means for developing an aerotropolis requires additional research, particularly from the viewpoint of long-term public policy and planning. In this study, a multiple criteria decision making model was applied to explore the key factors for successfully building an aerotropolis. We first applied the Decision-making Trial and Evaluation Laboratory based Analytical Network Process to construct the complex system and influential weights. A modified VIKOR method was then utilized to explore the gaps between the aspiration levels and the current situation. In addition, considering the uncertainty of decision-makers, fuzzy theory was integrated into the model. Data from the Taoyuan Aerotropolis in Taiwan were used to demonstrate this method. The results indicate that internationalization is the most crucial factor within the system, and that administrative efficiency has the highest degree of net influence. The largest weighted gap to the examined aspiration level is adequate regulation. Management implications are provided in the discussion.

A recent theory of information uncertainty (IU) postulates a negative (positive) relationship between IU and future returns (momentum returns). We extend this theory by showing that its predictions could be conditioned by differences in behavioral biases induced by culture. We find that greater IU does not necessarily result in lower future returns in China unlike in the U.S. In fact for some IU proxies, high IU firms have higher future returns. Second, we confirm earlier evidence of a weak momentum effect in the Chinese stock market which is consistent with the low level of individualism among Chinese investors reported in the literature. Third we find that momentum returns of firms with greater IU are not necessarily higher than firms with lower IU.

This study examines the relationship among analysts’ earnings forecast revisions, information uncertainty, and stock returns and provides new evidence that stock price drift occurs after analysts’ earnings forecast revisions. Using data from the Australian stock market over the period of 1992 to 2009, we find that the stocks with upward earnings revisions experience positive returns, while stocks with downward revisions have negative returns. The effect is more prominent in stocks with high information uncertainty. The results are robust after controlling for market conditions, seasonality, and risks. Our evidence supports the conservatism bias model that investors tend to underweight the public information, such as analysts’ earnings forecast revisions. Importantly, our evidence provides possible explanations about the violation of the efficient market hypothesis. Our results suggest that the conservative bias causes investors not to sufficiently update their beliefs and eventually results in subsequent return continuation as investors’ underreaction to analysts’ earnings forecast revision is stronger with higher information uncertainty.

We examine how information uncertainty surrounding IPO (initial public offering) firms influences earnings management and long-run stock performance. For low-information-uncertainty issuers, at-issue earnings’ management is positively related to subsequent unmanaged earnings and has no relationship to market reaction to earnings announcement and long-run stock performance following the offering. For high-information-uncertainty issuers, however, at-issue earnings’ management is unrelated to subsequent unmanaged earnings and negatively related to market reaction to earnings announcement and long-run stock performance following the offer. The evidence suggests that, on average, managers in low-information-uncertainty firms tend to engage in earnings’ management for informative purposes, while managers in high-information-uncertainty firms engage in earnings’ management for opportunistic purposes.

This paper examines the effect of income smoothing on information uncertainty, stock returns, and cost of equity. I show that income smoothing through both total accruals and discretionary accruals tends to reduce firms' information uncertainty, as measured by stock return volatility, analyst earnings forecast dispersion, and analyst earnings forecast error. Further, I provide evidence that stocks of income smoothing firms are priced with a premium. Controlling for earnings shocks and other firm characteristics, income smoothing firms have significantly higher abnormal returns around earnings announcement. In addition, I show that income smoothing reduces firms' implied cost of equity or expected returns. The result is more robust over short horizons up to two years.

This study investigates the association between industry information uncertainty and stock return comovement within industries. We test two predictions on industry comovement given correlated overweight among investors on past industry returns when there is greater industry-level uncertainty: (1) we find that stocks in high-uncertainty industries are more likely to move with other stocks in the same industry; (2) we find the higher prevalence of industry price continuation in high-uncertainty industries. The results suggest that stock comovement in industries with high information uncertainty is more likely driven by correlated bias in trading activities and is not related to common fundamental shocks.

Rising oil prices, steel prices - as well as the stronger dollar certainly impacted the financial markets during the latter part of May 2016. So also, the expected announcement of higher interest rates in June 2016 by the Federal Reserve. As well as illustrating the impact of financial shocks for emerging economies, this chapter also illustrates the impact of financial shocks for smaller – as well as more advanced economies. Having highlighted through the entire volume, the macroeconomic consequences of the changes and fluctuations of commodity prices – as reflected through the recent global financial market volatilities. This chapter will also focus on the main channels through which commodities price fluctuations affect business cycles in EMEs.

We examine how CEO turnover and changes in information uncertainty affect the price and non-price terms of bank loan contracts. Firms face worsened loan terms following the departure of a CEO. Interestingly, the negative effect that CEO departures have on loan terms largely stems from forced CEO turnovers. Following forced CEO departures, firms pay higher loan spreads, see an increase in covenants, and are more likely to be subject to collateral requirements, when compared to voluntary turnover and non-turnover matched firms. We find that changes in information uncertainty helps to explain the observed worsened terms following these dismissals. Furthermore, a superior capital market information environment helps to mitigate the adverse effect of the forced departure on bank loan terms and weakens the channel effect of accounting information quality. Our results are robust to a number of controls for endogeneity and outline the importance of public information for bank loan contracting.

In this paper we investigate the effects of informed trading (PIN) and information uncertainty in determining price momentum. We find that trading strategies based on buying high-uncertainty good-news stocks and shorting high-uncertainty bad-news stocks work well when limited to high-PIN stocks, while stocks with low-PIN do not exhibit price continuations, even when the uncertainty level of those stocks is high. In contrast, momentum returns are always significant for high-PIN stocks, irrespective of information uncertainty. Overall, we show that the informed trading effect is both independent of and stronger than that of information uncertainty in determining price momentum.

In this study, we examine some of the consequences of asset securitization. Specifically, using a sample of bank holding companies, we investigate whether the difficulty in assessing the true extent of risk transfer, between securitizing banks and investors in asset-backed securities, affects bank information uncertainty. We find that when market participants have a greater difficulty in estimating risk transfer, banks face greater information uncertainty (i.e., larger bid-ask spreads and analyst forecast dispersion). In addition, we find that this effect is mitigated for banks that operate in a higher quality information environment. We also find that banks that securitize financial assets have higher spreads and analyst forecast dispersion as compared to non-securitizing banks.

This study examines the effects of information uncertainty and information asymmetry on corporate bond yield spreads using American data from 2001 to 2006. Empirical results of this study show that investors charge a significant risk premium for both information uncertainty and information asymmetry when controlling for variables well known in the literature. The results are robust even when controlling for credit ratings. Finally, information uncertainty and asymmetry help structural-form credit models explain the yield spreads of bonds with short maturities.

A parsimonious two-factor model containing the market risk factor and a risk factor related to earnings information uncertainty has been developed to explain the seasonal regularity of January in international stock markets. This two-factor model shows apparently stronger power in explaining time-series behavior of stock returns and the cross-section of average stock returns in all major developed countries than do the competing models. Furthermore, the arbitrage residual return in January, which is the difference in the average residual returns between the smallest and largest size portfolios, is statistically insignificant in all the countries. These results indicate that the risk factor related to earnings information uncertainty plays a special role in explaining the seasonal pattern of stock returns in January, and that January might be a month that potentially tends to differentially reward stocks having uncertain earnings information. It could be argued, therefore, that large returns in January might be a risk premium for taking information uncertainty risk concerning earnings and unexpected earnings surprises faced at the earnings announcement, and that the previously reported strong January seasonality in stock returns might result from the use of misspecified models in adjusting for risk.

This paper explores the relationship between information uncertainty and auditor reputation revealed by the failure of Arthur Andersen (AA). AA’s reputation deteriorated considerably when it announced on January 10, 2002, that it had shredded documents related to its audit of Enron. AA’s demise was sealed on March 14, 2002, with its indictment for obstruction of justice. We find that on these dates the clients of AA and other Big Five auditors that are characterized by higher information uncertainty experience relatively larger share price declines compared to clients with lower information uncertainty. The findings suggest that the market relies more heavily on auditor reputation for higher information uncertainty firms, which implies that the value of an audit is greater when a firm is harder to value. Our results highlight the importance of information uncertainty in financial markets: where there is a shock to auditor reputation, firms with greater information uncertainty suffer the largest losses.

Investigating the effect of earnings announcement abnormal return and of abnormal trading volume on future returns for a large sample of European companies with both annual and interim announcements over 1997–2010, the author found that the two measures of market surprise are positively related to future abnormal returns, especially when information uncertainty is high. These two effects also appear to be complementary in that each retains some incremental predictive power for future returns.

The post-earnings-announcement drift has been the longest-standing anomaly in the finance and accounting literature. Several decades after this anomaly was first identified by Ball and Brown (1968), the strategy remains profitable. It has also withstood a variety of methodological checks. Although the anomaly violates the semi-strong form of market efficiency, Francis, LaFond, Olsson and Schipper (2007) (FLOS hereafter) argue that this need not imply investors’ irrationality, as stock-return predictability rationally arises under a learning model. FLOS test the validity of rational learning models as an explanation for the post-earnings-announcement-drift by examining the empirical implications of this hypothesis. FLOS draw three implications from the learning hypothesis. Their first hypothesis is that initial reaction to earnings surprise will be more muted, the larger is the information uncertainty about underlying earnings. This is because Bayesian investors place less weight on noisier (i.e., more uncertain) earnings information and more weight on their priors, making them under-react to new information. The testable prediction from this hypothesis is that the greater the information uncertainty, the smaller will be the magnitude of initial market reaction to earnings surprises. The second hypothesis is that, if information uncertainty gives rise to the post-earningsannouncement-drift, then it must be greater in firms with higher post-earningsannouncement-drift. As firms with extreme earnings surprises are the firms with most drift, they should also be firms with the most information uncertainty. The last hypothesis is that, controlling for earnings surprises, abnormal returns associated with the post-earnings-announcement-drift are larger for firms with higher information uncertainty. This hypothesis essentially states the consequences of hypothesis 1. That is, if initial under-reaction to earnings surprises is larger for firms with greater information uncertainty, as stated in hypothesis 1, then the subsequent corrections, which cause the post-earnings-announcement-drift, should also be larger for firms with greater information uncertainty. In addition to the above three implications of the learning hypothesis, FLOS conduct two additional analyses. First, they study the relation between their findings and those of

We examine whether rational investor responses to information uncertainty (IU) explain properties of and returns to the post-earnings-announcement-drift (PEAD) trading anomaly. Consistent with a rational learning explanation, we find that: (1) unexpected earnings (UE) signals that are characterized as having greater IU have more muted initial market reactions; (2) extreme UE portfolios are characterized by securities with higher IU than non-extreme UE portfolios; and (3) within the extreme UE portfolios, high IU securities are more prevalent and earn larger abnormal returns than low IU securities. Further tests show that prior evidence of greater PEAD profitability for higher idiosyncratic volatility securities is explained by the greater information uncertainty associated with these securities.

This paper is a survey of the theoretical and empirical literature on the determinants of credit spreads. In particular I discuss whether some observations on credit spreads can be related to information risk and ambiguity about the probabilistic structure of the process driving the fundamental value of credit securities. Corporate bond yield spreads are a compensation for the various sources of risk such as default, liquidity, taxes and systematic factors. Empirical studies show that corporate bond yield spreads are still larger than can be explained by these known determinants of credit spreads. I intertwine the literature on information risk premia and ambiguity premia and argue that some observations can possibly be related to such risks.

Prior literature observes that information uncertainty exacerbates investor underreaction behavior. In this paper, I investigate whether, as professional investment intermediaries, sell-side analysts suffer more behavioral biases in cases of greater information uncertainty. I show that greater information uncertainty predicts more positive (negative) forecast errors and subsequent forecast revisions following good (bad) news, which corroborates previous findings on the post-analyst-revision drift. The opposite effects of information uncertainty on forecast errors and subsequent forecast revisions following good versus bad news support the analyst underreaction hypothesis and are inconsistent with analyst forecast rationality or optimism suggested in prior literature.

There is substantial evidence of short-term stock price continuation, which the prior literature often attributes to investor behavioral biases such as underreaction to new information. This paper investigates the role of information uncertainty in price continuation anomalies and cross-sectional variations in stock returns. If short-term price continuation is due to investor behavioral biases, we should observe greater price drift when there is greater information uncertainty. As a result, greater information uncertainty should produce relatively higher expected returns following good news and relatively lower expected returns following bad news. My evidence supports this hypothesis. THERE IS SUBSTANTIAL EVIDENCE OF SHORT-TERM stock price continuation, which the prior literature often attributes to investor underreaction to new information. Examples include the positive serial correlation of returns at 3- to 12-month horizons (Jegadeesh and Titman (1993)), post-earnings announcement stock price drift in the direction indicated by the earnings surprise, and post-event return drift in the direction of the announcement date return. 1 In this paper I investigate how information uncertainty contributes to this phenomenon. By information uncertainty, I mean ambiguity with respect to the implications of new information for a firm’s value, which potentially stems from two sources: the volatility of a firm’s underlying fundamentals and poor information. 2 My main hypothesis is that if investors underreact to public

In this study, we examine the influence of real estate market sentiment, market-level uncertainty, and REIT-level uncertainty on cumulative abnormal earnings announcement returns over the 1995–2009 time period. We first document the relative coverage of analysts' earnings forecasts on U.S. REITs, as well as REITs from several countries (i.e., Australia, Belgium, Canada, France, Hong Kong, Japan, the Netherlands, and UK). We show that coverage outside of the U.S. is limited, and we consequently focus our analysis on U.S. REITs. We find strong evidence that earnings announcements contain pricing relevant information, with positive (negative) earnings surprises relative to analysts' forecasts resulting in significantly positive (negative) abnormal returns around the announcement date. Consistent with the findings from the broader equity market literature, we find limited evidence of a pre-announcement drift in the cumulative abnormal returns. However, in sharp contrast to the existing equity literature, we find no evidence of a post-earnings announcement drift in our aggregate sample or when the sample is restricted to the largest negative surprises. We find evidence of a post-earnings announcement drift for only the largest positive earnings surprises. These results are consistent with REIT returns more quickly impounding information relative to the broader equity market, in part because of the parallel private real estate market and because of the U.S. REIT structure and information environment. Finally, in our conditional regression analysis of cumulative abnormal returns, we find that real estate investor sentiment, market-wide uncertainty, and firm-level uncertainty significantly affect the magnitude of abnormal announcement returns and also influence the effect of unexpected earnings on abnormal returns.

PurposeThe purpose of this paper is to analyze the interaction between the availability of financial information and individuals' cognitive profiles to explain investors' herding behavior.Design/methodology/approachThe authors designed and conducted an experiment to observe the behavior of subjects in three settings, each with a different level of information.FindingsResults confirm that a dependence relation exists between information, investors' behavioral biases and the herding phenomenon. Moreover, the experiment shows that information concerning the number of previous transactions in the market is particularly relevant to explain herding propensity among investors. The findings indicate that the cognitive profile of investors is more relevant as the availability of information increases and the number of previous transactions in the market is low.Research limitations/implicationsFuture research should examine further the best way to measure the individual's cognitive profile and its interaction with information limitation in financial markets. The presence of high levels of uncertainty favors herding behavior regardless of inter‐individual differences, and only when the availability of information is high and the number of transactions is low does the subjects' cognitive profile explain the investors' herding behavior. Finally, it is observed that not all public information receives the same attention by investors. The attractiveness of public information requires further attention.Social implicationsThe herding phenomenon is difficult to anticipate because there are factors of a very diverse nature that intervene.Originality/valueThe research described in this paper measures investors' cognitive profile to identify the interaction between availability of information, cognitive profile and herding.

This is the first study to examine the post-earnings-announcement drift anomaly in a Real Estate Investment Trust (REIT) context. The efficient markets hypothesis suggests that unexpected earnings should be fully incorporated into asset prices soon after being publicly announced. We hypothesize that publicly announced earnings signals may be more certain for REITs due to the presence of a parallel (private) asset market, suggesting less drift for REIT stocks. However, we find a large REIT drift component that is both statistically and economically significant. Furthermore, while the initial earnings surprise response is more muted for REITs, we find that the magnitude of the drift is significantly larger for REITs than for ordinary common stocks (NonREITs). Thus, information does not appear to move between the private and public asset markets in such a way as to render REIT earnings signals more certain than NonREIT earnings signals.

This study examines the effect of the Jumpstart Our Business Startups Act (JOBS Act) on information uncertainty in IPO firms. The JOBS Act creates a new category of issuer, the Emerging Growth Company (EGC), and exempts EGCs from several disclosures required for non-EGCs. Our findings are consistent with proprietary cost concerns motivating EGCs to eliminate some of the previously mandatory disclosures, which increases information uncertainty in the IPO market, attracts investors who rely more on private information, and leads EGCs to provide additional post-IPO disclosures to mitigate the increased information uncertainty. Our results also are consistent with agency explanations, whereby EGCs exploit the JOBS Act provisions to avoid compensation-related disclosures, which results in larger IPO underpricing for such firms. Overall, we provide evidence on how reduced mandatory disclosure affects the IPO market.